hGA: Hybrid Genetic Algorithm in Fuzzy Rule-based Classification Systems for High-dimensional Problems
Yükleniyor...
Dosyalar
Tarih
2012
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science Bv
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules. Published by Elsevier B.V.
Açıklama
Anahtar Kelimeler
Fuzzy rule based classification systems, Genetic algorithms, Genetic fuzzy systems, Classification, Integer programming
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
12
Sayı
2
Künye
Kızılkaya Aydogan, E., Karaoglan, I., Pardalos, P. M., (2012). hGA: Hybrid Genetic Algorithm in Fuzzy Rule-based Classification Systems for High-dimensional Problems. Applied Soft Computing. 12(2), 800-806. doi:10.1016/j.asoc.2011.10.010
